Earlier quoted context omitted.
I had limited luck using the provided language model, but very good results when providing my own. So if you feel the results are poor, try building your own language model. AFAIK DeepSpeech works by using the neural net to detect characters from speech, and then the language model is used to try to make a sentence out of the character stream, by doing a kind of graph search. Thus if the language model doesn't contai…
For a software engineer with no experience in machine learning / AI, what does it mean to build your own language model? Does it require coding? Hundreds of hours of audio data from your own voice? A significant amount of computing power?
I should clarify this. As I mentioned, training the neural net part requires tons of audio and the corresponding text (and people should totally contribute[1], the resulting data sets are released to the public). The neural net in DeepSpeech is then used on an audio stream and outputs a stream of characters.
Turning that stream of characters into sentences is what the language model is for.
Training the neural net is very data and compute intensive, but fortunately Mozilla provides pre-trained models.
Generating the language model is relatively cheap. And if your target language shares sounds with English, you may get away with using the English-trained neural net but with a non-English language model.